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Research on Mobile Robot Path Planning Based on MSIAR-GWO Algorithm.

Danfeng Chen1, Junlang Liu1, Tengyun Li1

  • 1College of Mechanical Engineering and Automation, Foshan University, Foshan 528000, China.

Sensors (Basel, Switzerland)
|February 13, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces a novel Multi-Strategy Improved Artificial Rabbit Optimization Algorithm (MSIAR-GWO) to enhance mobile robot path planning. The improved algorithm demonstrates superior stability, accuracy, and convergence speed compared to traditional methods.

Keywords:
detour foraginggray wolf optimization algorithmparameter selectionpath planningreinforcement learning

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Area of Science:

  • Robotics
  • Artificial Intelligence
  • Optimization Algorithms

Background:

  • Path planning is crucial for mobile robot navigation efficiency and safety.
  • Traditional Gray Wolf Optimization (GWO) faces challenges like slow convergence and local optima.
  • Balancing exploration and exploitation remains difficult in practical GWO applications.

Purpose of the Study:

  • To propose a Multi-Strategy Improved Gray Wolf Optimization (MSIAR-GWO) algorithm for enhanced path planning.
  • To address the limitations of traditional GWO, including slow convergence and local optima.
  • To improve the efficiency and safety of mobile robot autonomous navigation.

Main Methods:

  • Introduced a nonlinear convergence factor with reinforcement learning for intelligent parameter configuration.
  • Implemented an adaptive position-update strategy with detour foraging and dynamic weights.
  • Incorporated an artificial rabbit optimization algorithm bypass foraging strategy with Brownian motion and Levy flight.
  • Utilized an elimination and relocation strategy based on stochastic center-of-gravity dynamic reverse learning for inferior individuals.

Main Results:

  • MSIAR-GWO demonstrated excellent stability, higher solution accuracy, and faster convergence on benchmark test functions.
  • The algorithm effectively planned shorter and smoother paths in complex raster map environments.
  • Compared to traditional algorithms, MSIAR-GWO showed superior optimization-seeking ability and robustness.

Conclusions:

  • The proposed MSIAR-GWO algorithm significantly improves upon traditional GWO for path planning.
  • MSIAR-GWO offers a robust and efficient solution for mobile robot autonomous navigation.
  • The integration of multiple strategies enhances convergence speed, accuracy, and global search capabilities.